Pile groups are extensively utilized as supports for many coastal structures, such as bridges, jetties, and oil production platforms. The problem of understanding the interaction effects within pile groups and predicting the breaking wave forces on them is considered in this paper, using experimental tests and machine learning-based predictive modeling. The restriction of previous studies on this important engineering problem is that the pile group arrangements considered are limited. Prediction methods are therefore developed only for specific pile group arrangements and do not incorporate the effect of the incident wave direction. In this study, to partially overcome this limitation, an extensive experimental investigation is conducted on 70 different pile group arrangements under six breaking wave conditions. Three pile group coefficients, characterized by the total, quasi-static, and dynamic forces, are introduced for a thorough assessment of the interaction effects within the pile group. First, the pile group coefficients for three basic arrangements (tandem, side-by-side, and staggered) are evaluated. The results reveal a sheltering effect in the tandem arrangement and an amplification effect in the side-by-side arrangement. However, the forces on the measured pile in the staggered arrangement resemble those on the isolated pile, with neither significant sheltering nor amplification effects observed. Then, the results for all arrangements highlight the significant effect of wave direction on the pile group coefficients for small inter-pile spacing. Finally, different machine learning algorithms are adopted to develop predictive models for the group coefficients. The XGBoost model demonstrates superior accuracy for predicting the total and quasi-static force coefficients, while the dynamic force coefficient remains challenging to predict accurately due to its stochastic nature.
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